High-entropy alloys (HEAs) are an emerging class of materials consisting of multiple principal elements. Due to entropic effects, HEAs are highly stable at elevated temperature with promising hardness, tensile strength, and corrosion resistance for potential structural applications in extreme environments. As there are an unlimited number of possible HEA compositions, discovery efforts benefit from a multifaceted data analytics approach, amendable to deep and machine learning. In this work, we focus on exploring and connecting near-equimolar compositions comprised of the main constituent elements Fe, Cr, Ni, Al, Si, and Cu with predicted mechanical properties and diffusion coefficients to salient material descriptors. Results are compiled to inform and validate multi-scale atomistic modeling to enable prediction of HEA compositions that exhibit high strength. Ultimately, this information will be used in the development of a statistically driven approach to differentiate compositions of interest during alloy design and testing for nuclear applications.