Molecular crystals have diverse applications including pharmaceuticals , organic electronics , optical materials , and materials for gas storage and separation . In all cases, the properties of molecular crystals depend on the crystal packing. For example, pharmaceutical molecules can have widely different solubilities depending on the crystalline form, and in organic electronics, charge transport is critically dependent on crystal packing. However, molecular crystal packing is notoriously difficult to predict because it is dictated by a range of weak, subtle intermolecular interactions, such as van der Waals forces, aromatic pi-stacking, and hydrogen bonds . This is a major hurdle for digital material design because if we cannot predict crystal structure then we cannot, by definition, predict the functional properties of the crystal. To address this challenge, crystal structure prediction (CSP) methods have been created to identify molecular crystals with specific target functionalities. For example, the energy-structure-function (ESF) maps have guided the synthesis of various functional molecular crystals . However, despite these successes, calculating the physical properties for each structure on an ESF map, or even a sub-set of low-energy structures, can be computationally demanding. This problem is two-fold: the prediction of lattice energy, or crystal stability, is itself computationally expensive, and the functional property calculations are usually even more expensive. To tackle this, there has been a surge of interest in machine learning (ML) techniques for the rapid prediction of materials properties and the elucidation of structure-property relationship at a fraction of the cost of first-principles methods, such as density functional theory (DFT).