Pairwise graphical comparison of several distributions

You could try to combine several different plotting methods and combine the results. Here's an example, which could be tweaked accordingly:

cors<-round(cor(samples),2) #correlations

# make layout for plot layout
laymat<-diag(1:5) #histograms
laymat[upper.tri(laymat)]<-6:15 #correlations
laymat[lower.tri(laymat)]<-16:25 #heatmaps

layout(laymat) #define layout using laymat

par(mar=c(2,2,2,2)) #define marginals etc.

# Draw histograms, tweak arguments of hist to make nicer figures
for(i in 1:5) 
  hist(samples[,i],main=names(samples)[i])

# Write correlations to upper diagonal part of the graph
# Again, tweak accordingly
for(i in 1:4)
  for(j in (i+1):5){
    plot(-1:1,-1:1, type = "n",xlab="",ylab="",xaxt="n",yaxt="n")
    text(x=0,y=0,labels=paste(cors[i,j]),cex=2)
    }

# Plot heatmaps, here I use kde2d function for density estimation
# image function for generating heatmaps
library(MASS)
for(i in 2:5)
  for(j in 1:(i-1)){
     k <- kde2d(samples[,i],samples[,j])
     image(k,col=heat.colors(1000))
    } 

edit: Corrected indexing on the last loop.