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Improving charge carrier mobilities in organic semiconductors is achallenging task that has hitherto primarily been tackled by empiricalstructural tuning of promising core compounds. Knowledge-based methods cangreatly accelerate such local exploration, while a systematic analysis of largechemical databases can point towards promising design strategies. Here, wedemonstrate such data mining by clustering an in-house database of >64.000organic molecular crystals for which two charge-transport descriptors, theelectronic coupling and the reorganization energy, have been calculated fromfirst principles. The clustering is performed according to the Bemis-Murckoscaffolds of the constituting molecules and according to the sidegroups withwhich these molecular backbones are functionalized. In both cases, we obtainstatistically significant structure-property relationships with certainscaffolds (sidegroups) consistently leading to favorable charge-transportproperties. Functionalizing promising scaffolds with favorable sidegroupsresults in engineered molecular crystals for which we indeed compute improvedcharge-transport properties.

Finding the Right Bricks for Molecular Lego: A Data Mining Approach to Organic Semiconductor Design