The ability to predict the properties of novel compounds prior to synthesis, and to understand how these properties depend on their structure, is of considerable importance in materials discovery and design. In this paper, we showed that MD simulations can accurately predict experimental packing density values of small organic molecules and we provided corresponding benchmark results to quantify this finding. We conducted a highthroughput MD screening of 100,000 compounds, which allowed us to train a DNN density prediction model. This DNN model accurately reproduces the MD data within the margins of MD's intrinsic error, while being nearly seven orders of magnitude faster than MD. This exceedingly efficient approach allowed us to rapidly obtain the density values of a 1.5 million compound screening library, which would have been prohibitively time consuming and well out of reach for MD. By analysing the large data set resulting from this study, we could elucidate structure-property relationships that determine the den-sity values. We identified prevalent moieties in the high and low density regime and could quantify the impact of heteroatoms (sulfur and nitrogen). Further, we evaluated the DNN learning curve for the density prediction with respect to the available training data and found a considerably lower data demand than we had anticipated. Following this lesson, we will in future studies employ an on-the-fly assessment of the learning curve and terminate the training data generation once we observe satisfactory saturation. This will allow us to alleviate the data generation bottleneck and make machine learning models an even more viable and attractive proposition. Overall, our study underscores the value of combining powerful machine learning approaches with traditional computational modeling for the generation of the necessary data. It also demonstrates the utility of our software ecosystem (including the ChemLG molecular library generator code, the ChemHTPS automated high-throughput in silico screening program, and the ChemML machine learning package) in facilitating and supporting research efforts of this nature.