Organic molecular crystals are critical for many pharmaceutical and functional materials. However, their discovery is hindered by the combinatorial complexity of molecular design, polymorphic packing, and crystallization conditions. This review describes how materials informatics and autonomous experimentation can accelerate discovery through an integrated molecule–crystal–function–optimization workflow. Molecular‐level structure–property modeling and machine learning (ML) for the low‐cost screening of large chemical spaces are outlined. Crystal‐level approaches for predicting crystal stability and functional properties, including machine learning interatomic potentials (MLIPs) and finite‐temperature molecular dynamics, are discussed. Crystal structure prediction (CSP) is reviewed as the key link between molecules and realizable crystal packing, emphasizing ML‐accelerated structure generation, stability ranking beyond 0 K lattice energies, and strategies for managing polymorph overprediction. Bayesian optimization (BO) and closed‐loop robotic platforms that couple computation with crystallization and characterization to enable autonomous laboratories are highlighted. Open challenges include data quality and negative‐result collection, MLIP transferability, BO scalability to high‐dimensional and multi‐objective settings, and standardized benchmarks needed to transform data‐driven organic crystal discovery into a practical pipeline. The ultimate vision is a prompt‐to‐materials autonomous discovery engine, where humans define objectives and then ML, crystal prediction, and robotic workflows iteratively optimize discoveries.
Taniguchi et al. (2026) studied this question.