Schizophrenia is a complex polygenic disorder with hundreds of genetic variants contributing to disease risk, most of which are in regulatory non-coding regions of the genome. These variants impact gene networks and biological pathways that control fundamental cellular processes and subcellular organisation beyond synapses and neurotransmitters. Our goal is to develop a systems-based approach using patient-derived olfactory neuronal stem cells (ONS cells) to identify optimal therapeutic interventions for schizophrenia. We generated multi-omics data from 57 samples of ONS cells (30 schizophrenia patients and 27 healthy controls). The data includes high-content cell imaging, whole genome sequencing, transcriptome profiling (long, small and circular RNAs), and proteome analysis. Our approach integrates genome-wide association studies (GWAS), Connectivity Map drug-induced expression signatures (CMap), ONS cell gene expression profiles, bioinformatics predictions of binding motif sequences, and correlation analyses. This comprehensive integration aims to identify disease-associated gene networks and cell traits, enabling the selection of FDA-approved drugs that can potentially reverse the dysfunctional cell traits and gene signatures in individual patients. Our results reveal that schizophrenia patient-derived ONS cells have significant differences in cell traits related to subcellular organisation and cell motion, including cell area and shape, mitochondria and endoplasmic reticulum morphology, and cell motility (travel distance and speed). We identify gene networks associated with cell adhesion and migration that correlate with genes identified in GWAS and CMap gene-drug signatures. Our systems-based drug discovery pipeline provides new insights into the mechanisms of FDA-approved drugs and assists clinicians in selecting optimal treatments based on individual stem cell responses.