In the search for semiconducting materials with tailored optoelectronic properties, organic co-crystals have emerged as particularly promising materials for integrated photonics, photovoltaic devices, and organic LEDs. Co-crystals are crystalline, single-phase materials composed of two or more molecular compounds that interact noncovalently. One class of these, charge transfer (CT) co-crystals, can be defined by CT interactions between molecular subunits of electron donor and acceptor molecules. Organic CT co-crystals are particularly interesting for the development of optoelectronic devices, exhibiting behaviors such as ambipolar charge transport, tunable emission, and room temperature ferroelectricity. In the vast space of molecular complexity, finding combinations of donors and acceptors that produce co-crystals with optimal properties has inspired significant interest. Researchers use the principles of rational design to identify structurefunction relationships and correlate properties of individual molecules to the properties of bulk materials. Modern approaches to exploring chemical space have also emerged, such as high-throughput screening and machine learning. These methods screen combinations of donor and acceptor molecules to predict materials' bulk physical properties from molecular metrics such as geometric and energetic parameters. Machine learning and high-throughput screening approaches are continually improving via inclusion of new structure-function relationships, driving a need for fundamental studies that identify such relationships through crystallographic structure determination, spectroscopic characterization, and electronic structure characterization.